ANALYZING VARIATION IN EXPRESSION PROFILES AND EQTLS FOR GENES ASSOCIATED WITH SCHIZOPHRENIA BETWEEN CORTICAL BRAIN REGIONS

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1 ANALYZING VARIATION IN EXPRESSION PROFILES AND EQTLS FOR GENES ASSOCIATED WITH SCHIZOPHRENIA BETWEEN CORTICAL BRAIN REGIONS BRENDAN STUBBS Biomedical Informatics, Stanford University Stanford, CA Schizophrenia is a debilitating psychological disorder that has unpredictable and often negative impacts on behavior, cognition and emotional wellbeing. 1 It has a 1% lifetime risk and is approximately 80% heritable. 2 There is an opportunity to unify the extensive work done in schizophrenia genome-wide association studies (GWAS) with expression quantitative trait locus (eqtl) studies to find functional differences in genes associated with schizophrenia between brain regions. Ultimately, discovering differences in brain region expression profiles and eqtls will allow future schizophrenia GWAS efforts to be more targeted (i.e., statistically more powerful) and help uncover the biological networks underlying schizophrenia etiology. Here, we tested whether expression profiles and regulatory loci for genes putatively associated with schizophrenia vary across two separate cortical brain regions. Introduction The genetic components that contribute to the heritability of schizophrenia are largely unknown. There has been promising evidence that the causal genetic factors may be: a combination of common variants (i.e., a polygenic model), rare structural variants (i.e., deletions and duplications), 3,4 copy number variations, 5 and/or rare alleles that are not currently examined under typical genome-wide association study (GWAS) methods. 6,7 The polygenic model of schizophrenia heritability is based on the theory that thousands of common single nucleotide polymorphisms (SNPs) contribute additively to the pathogenesis of the disease. 8 This model has been estimated to explain between 3 to 20% of the heritability of schizophrenia. 9 This is the conceptual underpinning for current schizophrenia-focused GWASs, which have led to the discovery of several common SNP loci and genes that are associated with schizophrenia. 10 SNPs identified in GWASs are often tested for their correlation with gene expression levels in separate expression quantitative trait locus (eqtl) studies. 11 These studies are generally guided by the intuitive assumption that if disease-associated SNPs are shown to correlate with gene expression, they are more likely to be a causal SNP for the disease. A small number of eqtl studies have investigated the regulation functions of SNPs that are putatively causal in schizophrenia pathogenesis. Psychiatric-focused eqtl studies have shown that assessing gene-snp correlations in human brain tissue can successfully produce functional annotations of hundreds of loci. 12,13 In addition, it has recently been demonstrated that eqtls in healthy human brain tissue are statistically enriched for SNPs associated with schizophrenia. 14 Different regions of the human brain exhibit distinctly different expression patterns and gene modules. 15 However, all large-scale eqtl studies of human brain tissue thus far have pooled samples from multiple brain cortical regions together. In these studies, the originating brain region of each sample has been treated as a covariate factor that is regressed out before testing for eqtls. 16 Only two previous studies have compared eqtls across different human brain regions; one suffered from a small sample size (i.e., n=20) 17 and one was restricted to a single cell type (i.e., astrocytes) 18. Here, we take the novel approach of using a relatively large (i.e., n=193), previouslypublished human brain eqtl data set to test whether expression profiles and regulatory loci (eqtls) for genes previously found to be associated with schizophrenia vary significantly between the temporal and frontal cortex.

2 2 Methods The data set we used was previously published by Myers et al. and contains matching genotype data and mrna expression profiles for healthy (i.e., no recorded mental illness), post-mortem brain cortex samples. There are 193 samples, of which 41 were taken from the frontal cortex and 136 were taken from the temporal cortex, each from separate individuals. Three parietal cortex samples and several unlabeled samples were not used for this analysis. Genotypes for all samples were obtained using the Affymetrix GeneChip Human Mapping 500K Array Set platform and include SNP genotypes at 500,056 loci. Due to the small number of gene-snp pairs tested, we did not filter the genotype data for call rates, allele frequencies or Hardy Weinberg equilibrium. mrna transcript expression levels were measured on the Illumina HumanRefseq-8 Expression BeadChip platform. The mrna data set is rank-invariant normalized and includes 14,078 gene probes. 24,357 probes were originally used; however, probes that were detected in less than 5% of samples were not included. 13 The mrna data set is available from GEO (GSE8919) and the matching genotype data is available from the University of Miami Miller School of Medicine website. 19 Schizophrenia is a late-onset disorder and is seen equally in both genders (although men generally experience an earlier onset), thus, we examined our data to ensure it was age-matched. Table 1: A summary of the cortical brain samples Brain region Total samples Average age at death Male Female Frontal Temporal To identify putative genes in the development of schizophrenia, we performed a literature review, 16 and included any genes that a single study had implicated as being putatively involved in the pathogenesis of schizophrenia. Our gene list was also largely informed by the only previous study that investigated cis-regulatory variation across brain regions. 17 We then eliminated any genes that were not available in the mrna data set. Table 2: A summary of genes putatively associated with schizophrenia included in our analysis Gene Evidence for schizophrenia association Sample size ZNF804A GWAS meta-analysis 20 21,274 cases, 38,675 controls NOS1 GWAS cases, 2,937 controls GWAS cases, 182 controls RGS4 Linkage and association study cases AKT1S1 Analysis of differential protein levels and phosphorylation cases TCF4 GWAS 10 2,663 cases, 13,498 controls GWAS 9 3,322 cases, 3,587 controls Differential expression We performed Significance Analysis of Microarrays (SAM) 25 to assess if the selected genes [Table 2] were expressed at statistically different levels between the frontal and temporal cortex samples. We labeled each sample in the mrna expression data set as either frontal or temporal (i.e., the response variable). We then ran SAM (delta = 1.14, seed = 100, 100 permutations) and isolated gene probes that were implicated as differentially expressed with a q-value (i.e., false-discovery rate) of 0. We then intersected this list of differentially expressed probes with our schizophrenia gene list to test for statistical enrichment in the union. eqtl determination To find eqtls and potentially reveal differences in regulatory elements between brain regions, we first identified potential nearby cis-acting SNPs for each schizophrenia-associated gene. We defined

3 3 nearby potential cis-regulatory SNPs as SNPs within 100,000kb of the beginning or end of a gene. 26,14 By only running regressions for gene-snp pairs in which the SNP has a high likelihood of interacting with the gene (i.e., closer on the chromosome), we limited the number of hypotheses we were testing for, and thereby, increased the statistical power of all associations found. After calculating major and minor alleles for each SNP in the genotype data set, we transformed the genotype SNP data for each sample into categorical numeric variables reflecting the number of major alleles present: 0 (homozygous minor), 1 (heterozygous), or 2 (homozygous major). If either allele in a given sample was unknown, we excluded the sample for that SNP. We matched the transformed SNP data to the mrna expression matrix by a predefined sample ID number and separated the two subsamples, frontal cortex and temporal cortex. We calculated the correlation (linear dependence) between each mrna expression probe paired with each of its nearby cis-snps with Pearson's correlation coefficient, Eq. (1). We also used analysis of variance (ANOVA) with SNP genotype as the independent variable and gene probe expression as the dependent variable; this method yielded identical results. (1) We performed eqtl determination analysis on each brain region subset independently, as well as on the composite sample. We deemed eqtls significant (P < 0.05) after correcting for multiple hypotheses by multiplying the p-value by the number of regressions (i.e., correlation tests) performed for the given gene. Figure 1: The number of SNPs tested for cis-regulatory action for each gene For each gene-snp comparison, we removed any sample that did not have either an mrna expression value or valid genotype, thus, the uncorrected correlation p-values are affected by the number of samples actually used in each test. We then checked literature sources such as PubMed, Google Scholar and SNPedia.com for any overlap in these identified eqtls with SNPs mentioned in any previous studies, including schizophrenia GWAS.

4 4 Figure 2: The pipeline of our analysis Results Differential expression SAM analysis revealed differential expression between the two brain regions in several genes. 36 genes were significantly (q-value = 0) more expressed in the frontal cortex and 56 genes were significantly more expressed in the temporal cortex. Of these significant genes, none were in our manually-curated list of schizophrenia-associated genes, thus, no statistical enrichment test was performed. This result does not support the hypothesis that schizophrenia-associated genes are differentially expressed across brain regions. In addition, since the two sample subsets reflect disparate sets of individuals, a qualitative evaluation of the differentially expressed genes (e.g., Gene Set Enrichment Analysis, Gene Ontology term enrichment) is not particularly meaningful.

5 5 Gene Figure 3: SAM results for differential expression between frontal and temporal cortex samples Table 3: The three most differentially expressed genes in each brain region Cortex region with greater expression Score (d) Fold change GNPDA2 Frontal TRPV4 Frontal CNDP1 Frontal USP22 Temporal PRKAR1B Temporal LAMC3 Temporal Accepted false discovery rate (qvalue) Several genes had relatively extreme differential expression between groups, and we added the top six differentially expressed genes to our subsequent eqtl analysis [Table 3]. Of these genes, the following have been putatively implicated in disease: GNPDA2 has been found to act in the central nervous system and is associated with obesity; 27 CNDP1 has been implicated in nephropathy in diabetics; 28 USP22 is involved in the cell cycle and is putatively involved in cancer pathogenesis. 29 Only GNPDA2 may be relevant for this analysis, but we included all six genes in the eqtl analysis as an exploratory initiative with the open hypothesis that differentially expressed genes may be more likely to be mediated by different regulatory loci in different brain regions. In addition, these six genes served as a heuristic comparison for the putative schizophrenia genes in our eqtl analysis. eqtl determination After correcting p-values for multiple hypothesis testing, we found four loci in the temporal region samples, no loci in the frontal cortex samples and three loci in the composite aggregate significant enough (P < 0.05) to be considered true eqtls. Full results of eqtl determination analysis can be found in the supplemental data files.

6 6 Table 4: Summary of significant gene-snp pairs Sample group Significant gene-snp pairs prior to p-value correction Significant gene-snp pairs after p-value correction Temporal cortex 45 4 Frontal cortex 47 0 Aggregate 67 3 Gene LAMC3 Table 5: Significant eqtls in the temporal cortex region samples (MAF = minor allele frequency) Chromosome: eqtl SNP Correlation Corrected SNP locus MAF MAF start/stop base p-value p-value region total Ch9: rs Ch9: / rs rs snp_a Figure 4: Boxplots of expression quartiles for each genotype at significant eqtls in the temporal cortex region samples

7 7 Table 6: Significant eqtls in the frontal cortex region samples (MAF = minor allele frequency) Gene Chromosome: start/stop base eqtl SNP Correlation p-value Corrected p-value SNP locus MAF region MAF total rs * *Not significant Figure 5: Boxplots of expression quartiles for each genotype at significant eqtls in the frontal cortex region samples Gene Table 7: Significant eqtls in the aggregated samples (MAF = minor allele frequency) Chromosome: eqtl SNP Correlation Corrected SNP locus start/stop base p-value p-value rs rs rs MAF total Figure 6: Boxplots of expression quartiles for each genotype at significant eqtls in the aggregated samples

8 8 Our literature searches revealed no evidence that any of these SNPs have been previously identified as eqtls or are associated with any other disease, including schizophrenia. Discussion By identifying no putative schizophrenia genes that were differentially expressed between temporal and frontal cortex samples, and only uncovering a small number of significant eqtls in each sample subset, we cannot develop any strong conclusions. As there were no significant eqtls found in the frontal cortex samples, our analysis is limited and we cannot confirm our hypothesis that different eqtls act in different brain regions on schizophrenia-associated genes. Regardless, the findings reported above do offer hope for future studies and may inform future analysis using this data set. First, the fourth most significant gene-snp pair in the temporal region (corrected P = ) matched the fifth most significant gene-snp pair in the frontal region (corrected P = ): and snp_a There is no rsid for this SNP and after examining its location (given in the.map loci file) in dbsnp and the UCSC Genome Browser, we were unable to uncover any additional information. Second, we found that of the top 100 SNPs (by p-value) for both regions, 27 were in common, where approximately 19 would be expected by random chance (after correcting for gene-snp pairs where the correlation coefficient was incomputable). In addition, all significant eqtls except one were found to be influencing the expression of, a gene in the major histocompatibility complex (MHC) on chromosome 6. The two strongest (i.e., largest sample size) schizophrenia GWASs recently demonstrated that the MHC contains a high concentration of schizophrenia-associated SNPs. 30,10 If the eqtls we identified are found to indeed correlate differently with expression between brain regions, these may be significant loci in the etiology of schizophrenia. The largest GWAS for psychiatric disorders to date (59,000 cases and 7,700 family trios) is currently being implemented. 31 It will be exciting to see if more informative gene targets are found or if the eqtls implicated in our study are shown to be associated with schizophrenia. Future work Our study was novel in its approach; however, it had several methodological shortcomings, which may be improved upon in future cross-brain region expression and eqtl studies. Multiple brain region samples should be taken from each individual, rather than separate brain samples from disparate individuals to control for confounding individual differences, as well as varying sample acquisition and tissue handling techniques. Regions outside the cortex, as well as stratified age groups should be investigated, particularly since schizophrenia usually has an age of onset in teenage or early adult years 1. Brain tissue is highly heterogeneous, with potentially thousands of different cell types. 32 Thus, ideally, each cell type should be investigated separately and measured simultaenously. 16 This will likely allow a more statistically-sound study in which microarray batch effects and individual differences may be mitigated. Our study was limited by a small sample size, particularly among the frontal cortex samples. Future studies would benefit from significantly larger sample sizes. Using a more robust data set, as described above, a correlation network of all brain-regionspecific eqtls, analogous to tissue-specific gene modules, could be constructed. There are more sophisticated techniques for multiple hypothesis correction that could be employed, 33,34 which we did not use, as we only tested a small number of genes (n = 12).

9 9 Although employing a broader definition of a cis-acting regulatory region (such as increasing the window for each gene to 2Mb) may diminish statistical power, it would be more feasible to employ effectively on a much larger, more robust data set. Acknowledgements Thanks to Pablo Sanchez Cordero and Ken Jung for generously taking time to discuss my project. Thanks to Atul Butte for a very fun and useful course. Thanks to Hunter Fraser and Chunyu Liu (University of Chicago) for answering my questions. Thanks to the Myers lab at University of Miami for freely supplying eqtl brain data. Appendix All analyses were performed in R with custom scripts contained in the file stubbs_217_project_scripts.r. Outside packages used were samr and fts. Full results can be found in the.zip file included with this paper and at References 1 American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (Revised 4th ed.). Washington, DC: APA. 2 Sullivan, P. F., Kendler,K. S.& Neale,M.C. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, (2003). 3 Walsh T, McClellan JM, McCarthy SE et al.rare structural variants disrupt multiple genes in neurodevelopmental pathways inschizophrenia. Science Apr 25;320(5875): Sebat J, Levy DL, McCarthy SE. Rare structural variants in schizophrenia: one disorder, multiple mutations; one mutation, multiple disorders. Trends Genet.2009 Dec;25(12): Review. 5 Stefansson H, Rujescu D, Cichon S et al. Large recurrent microdeletions associated with schizophrenia. Nature Sep 11;455(7210): McClellan JM, Susser E, King MC. Schizophrenia: a common disease caused by multiple rare alleles. Br J Psychiatry Mar;190: Review. 7 Author: Stubbs, B. 8 Gottesman, J Shields. A polygenic theory of schizophrenia. Proc Natl Acad Sci U S A July; 58(1): International Schizophrenia Consortium, Purcell SM, Wray NR, Stone JL,Visscher PM, O'Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature Aug 6;460(7256): Stefansson H, Ophoff RA, Steinberg S, Andreassen OA et al. Common variants conferring risk of schizophrenia. Nature Aug 6;460(7256): Ryten M, Trabzuni D, Hardy J. Genotypic analysis of gene expression in the dissection of the aetiology of complex neurological and psychiatric diseases. Brief Funct Genomic Proteomic May;8(3): Liu C, Cheng L, Badner JA, Zhang D, Craig DW, Redman M, Gershon ES. Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol Psychiatry Aug;15(8): Myers AJ, Gibbs JR, Webster JA et al. A survey of genetic human cortical gene expression. Nat Genet Dec;39(12): Richards AL, Jones L, Moskvina V, Kirov G, Gejman PV, Levinson DF, Sanders AR; Molecular Genetics of Schizophrenia Collaboration (MGS), International Schizophrenia Consortium (ISC), Purcell S, Visscher PM, Craddock N, Owen MJ, Holmans P, O'Donovan MC. Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain. Mol Psychiatry Feb Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH. Functional organization of the transcriptome in human brain. Nat Neurosci.2008 Nov;11(11): Liu C. Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases. Neurosci Bull Apr;27(2): Buonocore F, Hill MJ, Campbell CD, Oladimeji PB, Jeffries AR, Troakes C, Hortobagyi T, Williams BP, Cooper JD, Bray NJ. Effects of cis-regulatory variation differ across regions of the adult human brain. Hum Mol Genet Nov 15;19(22):

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